Part 1

Title

“Socio-Economic Factors Influencing Employment in the United States: A Comprehensive State-by-State Analysis”

Authors

  1. Abhay Prasanna Rao

  2. Srika Raja

  3. Neha

  4. Esha

  5. Niharika

Abstract (TL;DR)

This project investigates the impact of socio-economic factors on employment rates across U.S. states. Utilizing ACS 2021 data, we explore relationships between employment and variables like education, citizenship, and housing. Key findings include significant correlations that inform employment dynamics in the U.S.

Motivation

We aim to analyze various socio-economic factors influencing employment in the U.S. This study is crucial for understanding how different aspects like education, age, and housing contribute to employment rates, thereby aiding policymakers and researchers.

Summary

We have imported data set from the ACS survey. We have 6 child RMD files for this project which has the data analysis for the Employment, Education, Citizenship, Age, Housing, Disabilities Data Set (ACS 2021).
Further, we started exploring each data set in detail and then we started combining each data set with the employment to see what results we can expect. We did find many direct relationships with each data set on employment data set. We have put our concluding results in the Final Report to help us stand by with our conclusions.

Part 2:

Data Sets and Variables

The below data sets are from data.census.gov [ United States Census Bureau]. We shortlisted it based on ACS 2021, inclusive for all states in United States.

  1. Employment - K202301

    Variable Description
    Total Total Employment Data
    In Labor Force Total People in Labor Force
    Civilian labor force: Total People in Civilian Labor Force
    Employed Total People Employed
    Unemployed Total People Unemployed
    In Armed Forces Total People in Armed Forces
    Not in labor force Total People not in Labor Force

  2. Education - K201501

    Variable Description
    Education_Total_students Total Students in the Education Survery
    Education_Below_9th grade Number of students who have completed 9th grade
    Education_9th to 12th grade_no diploma Number of students who have completed 9th grade to 12th grade but no diploma
    Education_High_school_graduate Number of high school graduate students
    Education_Some college_no degree Number of people enrolled into some college but have not acquired a degree
    Education_Associates_degree Number of people with associates degree
    Education_Bachelors_degree Number of people with bachelors degree
    Education_Graduate_professional degree Number of people with Graduate Degree

  3. Citizenship - K200501

    Variable Description
    Total Total Number of people in survey
    U.S. citizen Number of US citizen in the survery
    Not a U.S. citizen Number of Non US Citizens in the survery

  4. Age - K200104

    Variable Description
    Total_age Total number of people in the age data frame
    Age_under_18 Total number of under 18 people
    “Age_18_to_24 People between 18 to 24
    Age_25_to_34 People between 25 to 34
    Age_35_to_44 People between 35 to 44
    Age_45_to_54 People between 45 to 54
    Age_55_to_64 People between 55 to 64
    Age_over_64 People over 64

  5. Housing - K202502

    Variable Description
    Total Total Number of People in housing data frame
    Owner Occupied Total Number of people who have their own home
    Renter Occupied Total Number of people who are renting a place

  6. Disabilities - K201803

    Variable Description
    Total_people Total Poeple in the data frame
    Total With Disabilities Total with disabilities
    Hearing Total with hearing problem
    Vision difficulty Total with vision problem
    cognative Total with cognative problem
    ambulatory difficulty Total with ambulatory difficlty
    Self-care difficulty Total with self care difficulty
    No Disability Total without disabilities 
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

Employment

## Warning: • You have not set a Census API key. Users without a key are limited to 500
## queries per day and may experience performance limitations.
## ℹ For best results, get a Census API key at
## http://api.census.gov/data/key_signup.html and then supply the key to the
## `census_api_key()` function to use it throughout your tidycensus session.
## This warning is displayed once per session.

Interpretations:
High Employment States: The states at the far left, such as Nebraska, Minnesota, and Iowa, show the highest employment rates, each appearing to exceed 60%. Low Employment States: On the right side, Puerto Rico, West Virginia, and Mississippi have the lowest employment rates depicted, with Puerto Rico showing a rate significantly lower than all states, possibly below 40%. Variability: The chart shows that there is a significant variability in employment rates across different states and territories. This could be due to a variety of factors such as economic policies, industrial diversity, population demographics, and educational attainment levels.

The above bar chart shows the number of people who are employed, in armed forces or unemployed for each state. We can observe that bigger cities like california, new york, texas etc have the highest number of people who are employed. We can also notice that unemployment while compared to employment is less in each city.

The above graph shows the unployment rates across different states. We can observe that most number of states have an unemployment rate between 1-4.5%

The above chart shows the distribution of employment rates in the form of a map of the united states for better visualization. We can see that states like UT,NE,MN have the highest employment rates.

Education

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) based on percentage

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(percentage_columns)
## 
##   # Now:
##   data %>% select(all_of(percentage_columns))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Citizenship

## Warning: Removed 1 rows containing missing values (`position_stack()`).

## Warning: Removed 1 rows containing missing values (`position_stack()`).

## Warning: Removed 1 rows containing missing values (`position_stack()`).

The ratio of US Citizens vs Non-US Citizens varies greatly across various states in the U.S. as you can observe from the graph above.In general there is a trend observed that , states with lesser US citizens have a lower employment rate.

Age

Age is one of the important social factor which affects the job market. Employers may discriminate against older workers, believing them to be less productive, adaptable, or tech-savvy. This can lead to age bias in hiring and promotion practices, affecting older workers’ job opportunities.Younger workers may be willing to accept lower wages than older workers, making them more cost-effective for employers. Contradistinction in some fields older workers often have decades of experience and accumulated wisdom in their field, making them valuable assets to any team. They may have a deeper understanding of industry trends, protocols, and best practices, leading to better problem-solving and decision-making skills. Due to their experience and expertise, older workers may require less training than younger colleagues, saving employers time and resources.

The above plot shows the Percentage of total population in a particular age group in the state.We can also observe that the proportion of population in each state in age groups over 64 year and under 18 years are higher compared to the other age groups.That is expected because the interval in that category is bigger than the others which are 10 years interval.

The above plot facets by the proportion of population a particular age group.Most of the times all the states have almost the same proportion of people in the different age groups.From this graph we can find if some state is an outlier for any age group.For example if we look at the district of Columbia we can see that it has a different trend compared to the other states of US in almost all the age groups.

Prime Working Age Group

Workers in their prime years, defined by the government as 25-54 years.This age has started dropping in most parts of the country since the late 1960s, with steeper declines during recessionary periods.In 1969, the labor force participation rate of men ages 25 to 54 was 96 percent, and in 2015, the rate was under 89 percent according to US bureau of labor statistics. So, the following graphs are intended to focus on this prime working age group in states of US in 2021.

The above graph focuses on the proportion of population in the age group 25 to 34 years in the different states of the US. Important inferences from this graph:

  • We see that District of Columbia is a outlier compared to the trend from rest of the country.

  • It is important that we look at the top five state in this graph because we see more or less the same state but in a different order when we look at the other categories of prime working ages. The top 5 states with more population in this age is:

    • Colorado

    • Alaska

    • Washington

    • California

    • Utah

  • The 5 state with the lowest proportion of population in this age group:

    • West Virginia

    • Vermont

    • Maine

    • Mississippi

    • Wyoming

The above graph focuses on the proportion of population in the age group 35 to 44 years in the different states of the US.This graph shows a pattern similar to the previous graph. Important inferences from this graph:

  • We see that District of Columbia is a outlier compared to the trend from rest of the country in this age group too.

  • It is important that we look at the top five state in this graph because we see more or less the same state but in a different order when we look at the other categories of prime working ages. The top 5 states with more population in this age is:

    • Colorado

    • Washington

    • Alaska

    • Texas

    • Oregon

    • Utah

  • The 5 state with the lowest proportion of population in this age group:

    • New Hampshire

    • Michigan

    • Vermont

    • Delaware

    • Maine

The above graph focuses on the proportion of population in the age group 45 to 54 years in the different states of the US.This graph shows a pattern that is a little different from the previous 2 graphs which suggest why the declining age of prime working group is a rising issue in the US. Important inferences from this graph:

  • We see that District of Columbia is not a outlier in this age group.

  • It is important that we look at the top five state in this graph because we see more or less the same state but in a different order when we look at the other categories of prime working ages. The top 5 states with more population in this age is:

    • New Jersey

    • Georgia

    • New Hampshire

    • North Carolina

    • Connecticuit

  • The 5 state with the lowest proportion of population in this age group:

    • North Dakota

    • South Dakota

    • Utah

    • Montana

    • Nebraska

This graph attempts to look at all the prime working ages at once. We see overall the top 5 state are:

  • Colorado

  • Washington

  • California

  • Nevada

  • Texas

Housing

The above two bar charts show’s us the distrubution of precentage of renter and owner occupied housing by state. We can see that states like district of columbia, new york, california have the highest number of people who rent the houses. And, states like west virginia, maine, michigan have high number of people who own their own property.

Disabilities

## # A tibble: 52 × 10
##    NAME          Total_people Total With Disabilit…¹ Hearing `Vision difficulty`
##    <chr>                <dbl>                  <dbl>   <dbl>               <dbl>
##  1 Alabama            4957633                 808071  208028              152798
##  2 Alaska              702154                  92390   33397               15748
##  3 Arizona            7174053                 972252  298849              180792
##  4 Arkansas           2974701                 517051  142133              105624
##  5 California        38724294                4324355 1140131              844049
##  6 Colorado           5715497                 640346  211803              120570
##  7 Connecticut        3557526                 427014  113490               78078
##  8 Delaware            987964                 130551   37933               25335
##  9 District of …       659979                  76754   14429               14569
## 10 Florida           21465883                2906367  812248              555361
## # ℹ 42 more rows
## # ℹ abbreviated name: ¹​`Total With Disabilities`
## # ℹ 5 more variables: cognative <dbl>, `ambulatory difficulty` <dbl>,
## #   `Self-care difficulty` <dbl>, `Independent living difficulty` <dbl>,
## #   `No Disability` <dbl>

In the bar chart titled “Disability Rates by State,” I notice a striking range of disability rates across the U.S. states and territories. Puerto Rico has the highest rate, over 20%, which is significantly higher than any state, while states such as West Virginia, Mississippi, and Alabama also have high rates, each above 15%. In stark contrast, Utah, New Jersey, California, and the District of Columbia are at the lower end of the spectrum, with rates around or below 10%. This variation suggests that factors like healthcare access, occupation-related risks, and demographic differences could play a role in these rates. From a policy standpoint, it seems crucial that states with higher disability rates may need to prioritize services and support systems for disabled individuals. However, I’m aware that the chart doesn’t break down the type or severity of disabilities, which would be essential for a more nuanced understanding and effective policy-making.

The bar chart presents a comparison of different types of disabilities among residents of Iowa, with ‘Ambulatory difficulty’ being the most prevalent. This suggests that mobility impairments are a significant challenge for a large number of Iowans. ‘Independent living difficulty’ also represents a substantial portion, indicating that many individuals may struggle with daily activities without assistance. Interestingly, ‘Vision difficulty’ is the least common disability, which may reflect effective preventive care or accessibility to vision correction. ‘Hearing’ and ‘Cognitive’ disabilities fall in the middle range, signifying that while they are less common than mobility and independent living issues, they still affect a considerable number of people. This gives us a indepth analysis for each specific state. Similarly we can find for other states and dive deeper into the analysis.

Title

“Socio-Economic Factors Influencing Employment in the United States: A Comprehensive State-by-State Analysis”

## `geom_smooth()` using formula = 'y ~ x'

## $x
## [1] "Percentage of Bachelors Degree Holders"
## 
## $y
## [1] "Employment Rate"
## 
## $title
## [1] "Relation between Bachelors Degree Holders and Employment Rate"
## 
## attr(,"class")
## [1] "labels"

The plot suggests a positive correlation where states with a higher percentage of Bachelor’s degree holders tend to have higher employment rates, although the relationship does not appear to be very strong. The spread of the data points is quite broad, especially in the middle range of the percentage of Bachelor’s degree holders, which implies there are other factors at play influencing employment rates beyond just higher education attainment. The confidence interval, shown by the shaded area, is quite wide, indicating a significant variation in the employment rate at any given level of Bachelor’s degree holders. This leads me to think that while education is an important factor in employment, it is certainly not the only one, and state-specific economic policies, industries, and other socioeconomic factors might also play crucial roles.

In labor force: Civilian labor force: Employed Unemployed In Armed Forces Not in labor force EmploymentRate UnemploymentRate NotInLaborForceRate Education_Total_students Education_Below_9th grade Education_9th to 12th grade_no diploma Education_High_school_graduate Education_Some college_no degree Education_Associates_degree Education_Bachelors_degree Education_Graduate_professional degree U.S. citizen Not a U.S. citizen Total_age Age_under_18 Age_18_to_24 Age_25_to_34 Age_35_to_44 Age_45_to_54 Age_55_to_64 Age_over_64 Total Owner Occupied Renter Occupied Total_people Total With Disabilities Hearing Vision difficulty cognative ambulatory difficulty Self-care difficulty Independent living difficulty No Disability DisabilityRate
In labor force: 1.00 1.00 1.00 0.97 0.73 0.99 -0.03 0.31 -0.02 1.00 0.94 0.98 0.97 0.99 0.98 1.00 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 0.99 0.98 1.00 0.98 0.99 1.00 0.99 0.98 0.97 0.99 0.98 0.99 0.99 1.00 -0.29
Civilian labor force: 1.00 1.00 1.00 0.98 0.72 0.99 -0.03 0.31 -0.02 1.00 0.94 0.98 0.97 0.99 0.98 1.00 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.98 1.00 0.98 0.99 1.00 0.99 0.98 0.97 0.99 0.98 0.99 0.99 1.00 -0.29
Employed 1.00 1.00 1.00 0.97 0.73 0.99 -0.02 0.30 -0.02 1.00 0.94 0.98 0.98 0.99 0.98 1.00 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 0.99 0.98 1.00 0.98 0.99 1.00 0.99 0.98 0.97 0.99 0.98 0.99 0.99 1.00 -0.29
Unemployed 0.97 0.98 0.97 1.00 0.68 0.97 -0.09 0.42 0.02 0.97 0.97 0.97 0.93 0.96 0.95 0.98 0.98 0.97 0.97 0.97 0.96 0.97 0.98 0.97 0.98 0.97 0.95 0.96 0.93 0.99 0.97 0.95 0.93 0.94 0.95 0.95 0.98 0.97 0.98 -0.26
In Armed Forces 0.73 0.72 0.73 0.68 1.00 0.72 -0.05 0.17 -0.04 0.72 0.71 0.74 0.66 0.75 0.70 0.73 0.71 0.72 0.72 0.73 0.74 0.74 0.74 0.74 0.73 0.70 0.68 0.71 0.70 0.72 0.73 0.71 0.72 0.73 0.71 0.71 0.71 0.70 0.73 -0.24
Not in labor force 0.99 0.99 0.99 0.97 0.72 1.00 -0.11 0.32 0.07 1.00 0.93 0.99 0.98 0.99 0.99 0.99 0.97 1.00 0.94 1.00 0.98 0.99 0.99 0.99 1.00 1.00 0.99 0.99 0.98 0.99 1.00 1.00 0.99 0.98 0.99 0.99 1.00 1.00 0.99 -0.21
EmploymentRate -0.03 -0.03 -0.02 -0.09 -0.05 -0.11 1.00 -0.45 -0.96 -0.06 -0.10 -0.13 -0.10 -0.05 -0.08 -0.02 -0.01 -0.07 -0.03 -0.05 -0.03 -0.04 -0.04 -0.04 -0.06 -0.07 -0.10 -0.05 -0.05 -0.05 -0.05 -0.13 -0.11 -0.18 -0.13 -0.16 -0.16 -0.15 -0.04 -0.80
UnemploymentRate 0.31 0.31 0.30 0.42 0.17 0.32 -0.45 1.00 0.22 0.32 0.35 0.32 0.29 0.28 0.29 0.33 0.37 0.31 0.34 0.31 0.29 0.30 0.32 0.31 0.32 0.32 0.31 0.31 0.28 0.34 0.31 0.31 0.27 0.32 0.31 0.32 0.35 0.34 0.31 0.03
NotInLaborForceRate -0.02 -0.02 -0.02 0.02 -0.04 0.07 -0.96 0.22 1.00 0.02 0.04 0.09 0.07 0.01 0.05 -0.03 -0.05 0.03 -0.04 0.01 -0.01 0.00 -0.01 -0.01 0.01 0.02 0.06 0.01 0.02 -0.01 0.01 0.10 0.08 0.14 0.10 0.13 0.11 0.11 0.00 0.87
Education_Total_students 1.00 1.00 1.00 0.97 0.72 1.00 -0.06 0.32 0.02 1.00 0.94 0.99 0.98 0.99 0.99 1.00 0.98 1.00 0.95 1.00 0.99 0.99 1.00 1.00 1.00 1.00 0.99 1.00 0.98 0.99 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 1.00 -0.26
Education_Below_9th grade 0.94 0.94 0.94 0.97 0.71 0.93 -0.10 0.35 0.04 0.94 1.00 0.96 0.87 0.94 0.90 0.94 0.92 0.93 0.99 0.94 0.95 0.95 0.96 0.96 0.95 0.92 0.90 0.92 0.88 0.96 0.94 0.91 0.91 0.93 0.92 0.91 0.95 0.93 0.95 -0.20
Education_9th to 12th grade_no diploma 0.98 0.98 0.98 0.97 0.74 0.99 -0.13 0.32 0.09 0.99 0.96 1.00 0.96 0.98 0.96 0.97 0.95 0.99 0.95 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.96 0.98 0.96 0.98 0.99 0.98 0.98 0.99 0.98 0.98 0.99 0.99 0.99 -0.18
Education_High_school_graduate 0.97 0.97 0.98 0.93 0.66 0.98 -0.10 0.29 0.07 0.98 0.87 0.96 1.00 0.96 0.98 0.96 0.94 0.98 0.87 0.98 0.97 0.97 0.96 0.97 0.98 0.99 0.99 0.99 0.99 0.96 0.98 0.99 0.99 0.97 0.99 0.99 0.97 0.98 0.97 -0.20
Education_Some college_no degree 0.99 0.99 0.99 0.96 0.75 0.99 -0.05 0.28 0.01 0.99 0.94 0.98 0.96 1.00 0.98 0.98 0.95 0.99 0.94 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.97 0.99 0.98 0.98 0.99 0.98 0.99 0.97 0.98 0.98 0.98 0.98 0.99 -0.25
Education_Associates_degree 0.98 0.98 0.98 0.95 0.70 0.99 -0.08 0.29 0.05 0.99 0.90 0.96 0.98 0.98 1.00 0.98 0.96 0.99 0.91 0.98 0.97 0.97 0.97 0.97 0.98 0.99 0.99 0.99 0.98 0.97 0.98 0.99 0.98 0.96 0.98 0.98 0.98 0.99 0.98 -0.24
Education_Bachelors_degree 1.00 1.00 1.00 0.98 0.73 0.99 -0.02 0.33 -0.03 1.00 0.94 0.97 0.96 0.98 0.98 1.00 0.99 0.99 0.95 0.99 0.98 0.99 0.99 0.99 1.00 0.99 0.98 0.99 0.97 0.99 0.99 0.98 0.97 0.96 0.98 0.97 0.98 0.98 1.00 -0.31
Education_Graduate_professional degree 0.98 0.98 0.98 0.98 0.71 0.97 -0.01 0.37 -0.05 0.98 0.92 0.95 0.94 0.95 0.96 0.99 1.00 0.97 0.93 0.98 0.96 0.97 0.97 0.97 0.98 0.98 0.97 0.97 0.95 0.98 0.98 0.95 0.94 0.93 0.95 0.95 0.97 0.97 0.98 -0.34
U.S. citizen 1.00 1.00 1.00 0.97 0.72 1.00 -0.07 0.31 0.03 1.00 0.93 0.99 0.98 0.99 0.99 0.99 0.97 1.00 0.94 1.00 0.99 1.00 0.99 1.00 1.00 1.00 0.98 1.00 0.99 0.99 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 1.00 -0.24
Not a U.S. citizen 0.95 0.95 0.95 0.97 0.72 0.94 -0.03 0.34 -0.04 0.95 0.99 0.95 0.87 0.94 0.91 0.95 0.93 0.94 1.00 0.95 0.95 0.95 0.96 0.96 0.95 0.93 0.91 0.93 0.88 0.96 0.95 0.91 0.91 0.91 0.91 0.91 0.94 0.93 0.95 -0.30
Total_age 1.00 1.00 1.00 0.97 0.73 1.00 -0.05 0.31 0.01 1.00 0.94 0.99 0.98 0.99 0.98 0.99 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.98 1.00 0.98 0.99 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 1.00 -0.26
Age_under_18 0.99 0.99 0.99 0.96 0.74 0.98 -0.03 0.29 -0.01 0.99 0.95 0.99 0.97 0.99 0.97 0.98 0.96 0.99 0.95 0.99 1.00 1.00 1.00 1.00 0.99 0.98 0.96 0.99 0.98 0.99 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 1.00 -0.27
Age_18_to_24 1.00 1.00 1.00 0.97 0.74 0.99 -0.04 0.30 0.00 0.99 0.95 0.99 0.97 0.99 0.97 0.99 0.97 1.00 0.95 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.97 0.99 0.98 0.99 1.00 0.99 0.98 0.98 0.99 0.98 0.98 0.98 1.00 -0.26
Age_25_to_34 1.00 1.00 1.00 0.98 0.74 0.99 -0.04 0.32 -0.01 1.00 0.96 0.99 0.96 0.99 0.97 0.99 0.97 0.99 0.96 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.97 0.99 0.97 0.99 1.00 0.98 0.98 0.97 0.98 0.98 0.99 0.98 1.00 -0.27
Age_35_to_44 1.00 1.00 1.00 0.97 0.74 0.99 -0.04 0.31 -0.01 1.00 0.96 0.99 0.97 0.99 0.97 0.99 0.97 1.00 0.96 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.97 0.99 0.98 0.99 1.00 0.98 0.98 0.98 0.98 0.98 0.99 0.98 1.00 -0.27
Age_45_to_54 1.00 1.00 1.00 0.98 0.73 1.00 -0.06 0.32 0.01 1.00 0.95 0.99 0.98 0.99 0.98 1.00 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.98 1.00 0.98 0.99 1.00 0.99 0.98 0.98 0.99 0.99 0.99 0.99 1.00 -0.26
Age_55_to_64 0.99 1.00 0.99 0.97 0.70 1.00 -0.07 0.32 0.02 1.00 0.92 0.98 0.99 0.98 0.99 0.99 0.98 1.00 0.93 1.00 0.98 0.99 0.99 0.99 1.00 1.00 0.99 1.00 0.99 0.99 1.00 0.99 0.98 0.97 0.99 0.99 0.99 0.99 0.99 -0.26
Age_over_64 0.98 0.98 0.98 0.95 0.68 0.99 -0.10 0.31 0.06 0.99 0.90 0.96 0.99 0.97 0.99 0.98 0.97 0.98 0.91 0.98 0.96 0.97 0.97 0.97 0.98 0.99 1.00 0.99 0.98 0.97 0.98 0.99 0.98 0.96 0.98 0.99 0.98 0.99 0.98 -0.23
Total 1.00 1.00 1.00 0.96 0.71 0.99 -0.05 0.31 0.01 1.00 0.92 0.98 0.99 0.99 0.99 0.99 0.97 1.00 0.93 1.00 0.99 0.99 0.99 0.99 1.00 1.00 0.99 1.00 0.99 0.99 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 1.00 -0.26
Owner Occupied 0.98 0.98 0.98 0.93 0.70 0.98 -0.05 0.28 0.02 0.98 0.88 0.96 0.99 0.98 0.98 0.97 0.95 0.99 0.88 0.98 0.98 0.98 0.97 0.98 0.98 0.99 0.98 0.99 1.00 0.96 0.98 0.99 0.99 0.97 0.99 0.99 0.97 0.98 0.98 -0.25
Renter Occupied 0.99 0.99 0.99 0.99 0.72 0.99 -0.05 0.34 -0.01 0.99 0.96 0.98 0.96 0.98 0.97 0.99 0.98 0.99 0.96 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.97 0.99 0.96 1.00 0.99 0.98 0.97 0.96 0.98 0.97 0.99 0.98 0.99 -0.27
Total_people 1.00 1.00 1.00 0.97 0.73 1.00 -0.05 0.31 0.01 1.00 0.94 0.99 0.98 0.99 0.98 0.99 0.98 1.00 0.95 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.98 1.00 0.98 0.99 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 1.00 -0.26
Total With Disabilities 0.99 0.99 0.99 0.95 0.71 1.00 -0.13 0.31 0.10 0.99 0.91 0.98 0.99 0.98 0.99 0.98 0.95 0.99 0.91 0.99 0.98 0.99 0.98 0.98 0.99 0.99 0.99 0.99 0.99 0.98 0.99 1.00 1.00 0.99 1.00 1.00 0.99 0.99 0.99 -0.17
Hearing 0.98 0.98 0.98 0.93 0.72 0.99 -0.11 0.27 0.08 0.99 0.91 0.98 0.99 0.99 0.98 0.97 0.94 0.99 0.91 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.99 0.99 0.97 0.99 1.00 1.00 0.99 1.00 0.99 0.98 0.98 0.98 -0.18
Vision difficulty 0.97 0.97 0.97 0.94 0.73 0.98 -0.18 0.32 0.14 0.98 0.93 0.99 0.97 0.97 0.96 0.96 0.93 0.98 0.91 0.98 0.98 0.98 0.97 0.98 0.98 0.97 0.96 0.98 0.97 0.96 0.98 0.99 0.99 1.00 0.99 0.99 0.98 0.98 0.98 -0.11
cognative 0.99 0.99 0.99 0.95 0.71 0.99 -0.13 0.31 0.10 0.99 0.92 0.98 0.99 0.98 0.98 0.98 0.95 0.99 0.91 0.99 0.98 0.99 0.98 0.98 0.99 0.99 0.98 0.99 0.99 0.98 0.99 1.00 1.00 0.99 1.00 1.00 0.99 0.99 0.99 -0.16
ambulatory difficulty 0.98 0.98 0.98 0.95 0.71 0.99 -0.16 0.32 0.13 0.99 0.91 0.98 0.99 0.98 0.98 0.97 0.95 0.99 0.91 0.99 0.98 0.98 0.98 0.98 0.99 0.99 0.99 0.99 0.99 0.97 0.99 1.00 0.99 0.99 1.00 1.00 0.99 0.99 0.98 -0.14
Self-care difficulty 0.99 0.99 0.99 0.98 0.71 1.00 -0.16 0.35 0.11 0.99 0.95 0.99 0.97 0.98 0.98 0.98 0.97 0.99 0.94 0.99 0.98 0.98 0.99 0.99 0.99 0.99 0.98 0.99 0.97 0.99 0.99 0.99 0.98 0.98 0.99 0.99 1.00 1.00 0.99 -0.16
Independent living difficulty 0.99 0.99 0.99 0.97 0.70 1.00 -0.15 0.34 0.11 0.99 0.93 0.99 0.98 0.98 0.99 0.98 0.97 0.99 0.93 0.99 0.98 0.98 0.98 0.98 0.99 0.99 0.99 0.99 0.98 0.98 0.99 0.99 0.98 0.98 0.99 0.99 1.00 1.00 0.99 -0.16
No Disability 1.00 1.00 1.00 0.98 0.73 0.99 -0.04 0.31 0.00 1.00 0.95 0.99 0.97 0.99 0.98 1.00 0.98 1.00 0.95 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.98 1.00 0.98 0.99 1.00 0.99 0.98 0.98 0.99 0.98 0.99 0.99 1.00 -0.27
DisabilityRate -0.29 -0.29 -0.29 -0.26 -0.24 -0.21 -0.80 0.03 0.87 -0.26 -0.20 -0.18 -0.20 -0.25 -0.24 -0.31 -0.34 -0.24 -0.30 -0.26 -0.27 -0.26 -0.27 -0.27 -0.26 -0.26 -0.23 -0.26 -0.25 -0.27 -0.26 -0.17 -0.18 -0.11 -0.16 -0.14 -0.16 -0.16 -0.27 1.00 
## `geom_smooth()` using formula = 'y ~ x'

## Loading required package: viridisLite
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## Attaching package: 'viridis'
## The following object is masked from 'package:scales':
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## The following object is masked from 'package:maps':
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##     unemp
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## `geom_smooth()` using formula = 'y ~ x'

Part 3

Results

  1. Employment vs Education

The scatter plot labeled “Employment vs Education Levels” shows a positive correlation between the number of individuals with a Bachelor’s degree and the number of employed individuals. As I examine the plot, I notice that as the number of individuals with a Bachelor’s degree increases, so does the number of employed individuals, suggesting that higher education levels may be associated with better employment outcomes. The data points seem to form a rising trend, especially noticeable in the lower to middle range of the number of individuals surveyed. However, towards the higher end of the scale, the increase in employment with respect to the number of Bachelor’s degree holders becomes less pronounced. This could imply diminishing returns of higher education on employment at a certain point, or it might reflect a saturation of highly educated individuals in the job market

  1. Employment Vs Housing

The bar chart titled “Employed Individuals vs States by Housing Type” displays a comparison of employed individuals in various U.S. states, differentiated by housing type—owner occupied versus renter occupied. As I analyze the chart, I observe that in most states, the number of employed individuals living in owner-occupied housing is higher than those in renter-occupied housing. This could suggest a correlation between home ownership and employment status, which may be due to a variety of economic and social factors, such as the stability that home ownership can provide or the possibility that employed individuals have a higher purchasing power to buy homes. Notably, in states like California and Texas, the bars representing owner-occupied housing are significantly taller, which might reflect a combination of high employment rates and a culture or economy that favors home ownership. On the other hand, the District of Columbia stands out with a higher proportion of employed individuals in renter-occupied housing, which could reflect urban real estate trends where renting is more common due to high property costs or lifestyle choices. Overall, the graph suggests a complex relationship between employment and housing type, influenced by state-specific economic conditions and housing markets.

  1. Citizenship Vs Employmennt

The bar chart, titled “Employed Individuals vs States by Citizenship Status,” shows the number of employed individuals in each state, categorized by whether they are U.S. citizens or not. Looking at the chart, I see that U.S. citizens make up the majority of employed individuals in every state, which is to be expected given the larger population of citizens versus non-citizens. However, the proportion of employed non-citizens is noticeable, especially in states like California and Texas, which may reflect these states’ larger immigrant populations and their contributions to the workforce. The data also shows that in states like the District of Columbia, the number of employed non-citizens is relatively high compared to the total employed population, indicating diverse labor pools in these areas. This graph underscores the significant role that non-citizens play in the U.S. labor market, particularly in states with large urban centers or industries that attract foreign workers.

Conclusions

This study provides insights into the socio-economic factors affecting employment in the U.S. Limitations include data scope and potential biases. Future work could explore more granular data and additional variables.

Part 4

Data Source

  1. Data from the American Community Survey (ACS) 2021.
  2. Scripts for data cleaning and analysis are included in the repository.
  3. 6 Child RMD files included in the repo, which support the DS202_final_project.rmd file

References

  • American Community Survey (ACS)

  • United States Census Bureau (data.census.gov)